Chapters:
– 00:00:00 – Intro
– 00:01:21 – What is Machine Learning
– 00:03:03 – Mathematical Modeling
– 00:08:15 – Plan for Today
– 00:10:32 – Our First Model
– 00:12:24 – Training Data for the Model
– 00:17:05 – Initializing the Model
– 00:19:52 – Measuring How Well Model Works
– 00:27:56 – Improving the Cost Function
– 00:32:27 – Approximating Derivatives
– 00:41:25 – Training Process
– 00:45:59 – Artifician Neurons
– 00:50:11 – Adding Bias to the Model
– 00:56:16 – More Complex Model
– 00:58:41 – Simple Logic Gates Model
– 01:06:04 – Activation Function
– 01:15:24 – Troubleshooting the Model
– 01:25:04 – Adding Bias to the Gates Model
– 01:27:36 – Plotting the Cost Function
– 01:29:28 – Muxiphobia
– 01:31:43 – How I Understand Bias
– 01:33:20 – Other Logic Gates
– 01:36:13 – XOR-gate with 1 neuron
– 01:38:46 – XOR-gate with multiple neurons
– 01:49:14 – Coding XOR-gate model
– 01:57:53 – Human Brain VS Artificial Neural Network
– 02:00:26 – Continue coding XOR-gate model
– 02:15:14 – Non-XOR-gates with XOR Architecture
– 02:18:30 – Looking Inside of Neural Network
– 02:24:57 – Arbitrary Logic Circuits
– 02:27:23 – Shapes Classifier
– 02:29:42 – Better Representation of Neural Networks
– 02:30:36 – Outro
– 02:30:50 – Smooch
References:
– https://github.com/tsoding/perceptron
– Notes: https://github.com/tsoding/ml-notes
Support:
– BTC: bc1qj820dmeazpeq5pjn89mlh9lhws7ghs9v34x9v9
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